
# Warning filter
import warnings
warnings.filterwarnings("ignore")

# data manipulation libraries for Python
import pandas as pd
import numpy as np

# data visualisation libraires for Python
import matplotlib.pyplot as plt
import seaborn as sns

# statistical libraries for Python
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant

# prediction libraries for Python (Train/Test + Tree)
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    plot_confusion_matrix,
    make_scorer,
    roc_auc_score,
    roc_curve,
) 
# disable display column & row limits
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 250)


# upload and create aq clean copy of the data

innrec = pd.read_csv('INNHotelsGroup.csv')
data = innrec.copy()


# view first five rows of data
data.head()


# view last five rows of data
data.tail()


# I get the same random results every time
np.random.seed(1)
data.sample(n=50)


# view the size of the data set
data.shape

(36275, 19)


# view the data types of the data set
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB


# check for duplicate values
data[data.duplicated()].count()

Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64


# checking for NUll values... data is complete!!!
data.isnull().sum()

Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64


# drop the 'Booking ID' columns from the data set. 
data = data.drop('Booking_ID', axis=1)


# view what are the values in object data types
cat_columns = ['type_of_meal_plan', 'room_type_reserved', 'market_segment_type', 'booking_status']
for i in cat_columns:
    print(data[i].value_counts())
    print("*" * 50)

Meal Plan 1     27835
Not Selected     5130
Meal Plan 2      3305
Meal Plan 3         5
Name: type_of_meal_plan, dtype: int64
**************************************************
Room_Type 1    28130
Room_Type 4     6057
Room_Type 6      966
Room_Type 2      692
Room_Type 5      265
Room_Type 7      158
Room_Type 3        7
Name: room_type_reserved, dtype: int64
**************************************************
Online           23214
Offline          10528
Corporate         2017
Complementary      391
Aviation           125
Name: market_segment_type, dtype: int64
**************************************************
Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64
**************************************************


data.describe()


data.describe(include = ['object'])


# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


# function to plot a boxplot and a histogram along the same scale.


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


def stacked_barplot(data, predictor, target, perc=False):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
  
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5,))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


labeled_barplot(data, 'arrival_month', perc=True, n=None)


labeled_barplot(data, 'market_segment_type', perc=True, n=None)


histogram_boxplot(data, 'avg_price_per_room')


# how many free rooms does the hotel give away?
data[data['avg_price_per_room']==0]


data.loc[data['avg_price_per_room']==0, 'market_segment_type'].value_counts()

Complementary    354
Online           191
Name: market_segment_type, dtype: int64


sns.boxplot(data=data, y='avg_price_per_room' , x='market_segment_type')

<AxesSubplot:xlabel='market_segment_type', ylabel='avg_price_per_room'>


labeled_barplot(data, 'booking_status', perc=True, n=None)


stacked_barplot(data,'repeated_guest','booking_status')

booking_status  Canceled  Not_Canceled    All
repeated_guest                               
All                11885         24390  36275
0                  11869         23476  35345
1                     16           914    930
------------------------------------------------------------------------------------------------------------------------


sns.catplot(data=data, y='no_of_special_requests', hue='booking_status', kind='count' )

<seaborn.axisgrid.FacetGrid at 0x29a7c7b6370>


labeled_barplot(data, 'no_of_adults', perc=True)


labeled_barplot(data, 'no_of_children', perc=True)


labeled_barplot(data, 'no_of_weekend_nights', perc=True)


labeled_barplot(data, 'no_of_week_nights', perc=True)


labeled_barplot(data, 'required_car_parking_space', perc=True)


labeled_barplot(data, 'room_type_reserved', perc=True)


labeled_barplot(data, 'type_of_meal_plan', perc=True)


labeled_barplot(data, 'lead_time', perc=True)


histogram_boxplot(data, 'no_of_previous_cancellations')


histogram_boxplot(data, 'no_of_previous_bookings_not_canceled')


plt.figure(figsize=(20,10))
sns.heatmap(
data.corr(), annot=True, vmin=-1, vmax=1, fmt='.2f')

<AxesSubplot:>


# how does lead time effect cancellation
sns.catplot(data=data, x='lead_time', hue='booking_status', kind='count' )
plt.xticks(rotation=45)
plt.show()


# do weekends v weekday have differnt patterns on cancelation?
sns.catplot(data=data, y='no_of_week_nights', hue='booking_status', kind='count' )
plt.show()


sns.catplot(data=data, y='no_of_weekend_nights', hue='booking_status', kind='count' )
plt.show()


# having established that some months are busier than others, does priceing for that demand exist in the data? 
plt.figure(figsize=(10, 5))
sns.lineplot(data=data, x='arrival_month', y='avg_price_per_room')
plt.show()


# New column for lenght of stay
data['length_stay'] = data['no_of_weekend_nights'] + data['no_of_week_nights']


sns.pairplot(data[['no_of_weekend_nights','no_of_week_nights','required_car_parking_space',
                   'lead_time','avg_price_per_room','no_of_special_requests','type_of_meal_plan',
                   'room_type_reserved','market_segment_type','booking_status','length_stay']]);


data.loc[data['booking_status']=='Not_Canceled','booking_status'] = False
data.loc[data['booking_status']=='Canceled','booking_status'] = True


numeric_columns = data.select_dtypes(include=np.number).columns.tolist()
# drop column because they were either time, or not helpful
numeric_columns.remove("arrival_year")


plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    data.boxplot() 

plt.xticks(rotation=45)
plt.show()


#Solving the IQR fro avg price room
quartiles = np.quantile(data['avg_price_per_room'][data['avg_price_per_room'].notnull()], [.25, .75])
power_4iqr = 4 * (quartiles[1] - quartiles[0])
print(f'Q1 = {quartiles[0]}, Q3 = {quartiles[1]}, 4*IQR = {power_4iqr}')
outlier_powers = data.loc[np.abs(data['avg_price_per_room'] - data['avg_price_per_room'].median()) > power_4iqr, 'avg_price_per_room']
outlier_powers.shape

Q1 = 80.3, Q3 = 120.0, 4*IQR = 158.8

(49,)


# creating a list of columns
dist_cols = [
    item for item in data.select_dtypes(include=np.number).columns 
]

plt.figure(figsize=(15, 45))
#looping the list and ploting histograms
for i in range(len(dist_cols)):
    plt.subplot(12, 3, i + 1)
    plt.hist(data[dist_cols[i]], bins=50)
    plt.tight_layout()
    plt.title(dist_cols[i], fontsize=15)

plt.show()


data2 = data.copy()
# removing because they are close to normal
dist_cols.remove('no_of_week_nights')
dist_cols.remove('no_of_adults')
dist_cols.remove('length_stay')
dist_cols.remove('avg_price_per_room')
# removing becasue they are boolean or time related. 
dist_cols.remove('arrival_year')
dist_cols.remove('required_car_parking_space')
dist_cols.remove('arrival_date')
dist_cols.remove('arrival_month')
dist_cols.remove('repeated_guest')
# removing becasue I have a different treatment in mind
dist_cols.remove('lead_time')


# using log transforms on some columns
for col in dist_cols:
    data2[col + "_log"] = np.log(data2[col] + 1)

# dropping the original columns
data2.drop(dist_cols, axis=1, inplace=True)
data2.head()


# viewing the distributions after the log transformation. 
dist_cols = [
    item for item in data2.select_dtypes(include=np.number).columns 
]

#  plot histogram of all numeric columns

plt.figure(figsize=(15, 45))

for i in range(len(dist_cols)):
    plt.subplot(12, 3, i + 1)
    plt.hist(data2[dist_cols[i]], bins=50)
    sns.histplot(data=data2, x=dist_cols[i], kde=True)  
    plt.tight_layout()
    plt.title(dist_cols[i], fontsize=25)

plt.show()


# OneHotEncoding catergorical variables
dummy_data = pd.get_dummies (
    data2,
    columns = [
        'type_of_meal_plan',
        'room_type_reserved',
        'market_segment_type',
    ],
    drop_first=True,
)
dummy_data.head()


dummy_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 29 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  int64  
 1   no_of_week_nights                         36275 non-null  int64  
 2   required_car_parking_space                36275 non-null  int64  
 3   lead_time                                 36275 non-null  int64  
 4   arrival_year                              36275 non-null  int64  
 5   arrival_month                             36275 non-null  int64  
 6   arrival_date                              36275 non-null  int64  
 7   repeated_guest                            36275 non-null  int64  
 8   avg_price_per_room                        36275 non-null  float64
 9   booking_status                            36275 non-null  object 
 10  length_stay                               36275 non-null  int64  
 11  no_of_children_log                        36275 non-null  float64
 12  no_of_weekend_nights_log                  36275 non-null  float64
 13  no_of_previous_cancellations_log          36275 non-null  float64
 14  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 15  no_of_special_requests_log                36275 non-null  float64
 16  type_of_meal_plan_Meal Plan 2             36275 non-null  uint8  
 17  type_of_meal_plan_Meal Plan 3             36275 non-null  uint8  
 18  type_of_meal_plan_Not Selected            36275 non-null  uint8  
 19  room_type_reserved_Room_Type 2            36275 non-null  uint8  
 20  room_type_reserved_Room_Type 3            36275 non-null  uint8  
 21  room_type_reserved_Room_Type 4            36275 non-null  uint8  
 22  room_type_reserved_Room_Type 5            36275 non-null  uint8  
 23  room_type_reserved_Room_Type 6            36275 non-null  uint8  
 24  room_type_reserved_Room_Type 7            36275 non-null  uint8  
 25  market_segment_type_Complementary         36275 non-null  uint8  
 26  market_segment_type_Corporate             36275 non-null  uint8  
 27  market_segment_type_Offline               36275 non-null  uint8  
 28  market_segment_type_Online                36275 non-null  uint8  
dtypes: float64(6), int64(9), object(1), uint8(13)
memory usage: 4.9+ MB


dummied_cut = pd.cut(dummy_data['lead_time'], 5, labels=['lat_min','short','med','long','advanced'])
dummied_cut.head(10)

0        med
1    lat_min
2    lat_min
3        med
4    lat_min
5       long
6    lat_min
7    lat_min
8      short
9    lat_min
Name: lead_time, dtype: category
Categories (5, object): ['lat_min' < 'short' < 'med' < 'long' < 'advanced']


data3 = pd.merge(dummy_data, dummied_cut, left_index=True, right_index=True)

data3.head().T


# dropping time variables and lead_time_x since it has been binned into 5 columns.
data3_5 = data3.drop(['lead_time_x','arrival_date', 'arrival_year'], axis=1)


data4 = pd.get_dummies (
    data3_5,
    columns = [
        'lead_time_y',  
    ],
    drop_first=True,
)
data4.head().T


data4 = data4.astype(float)


data4.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 30 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  float64
 1   no_of_week_nights                         36275 non-null  float64
 2   required_car_parking_space                36275 non-null  float64
 3   arrival_month                             36275 non-null  float64
 4   repeated_guest                            36275 non-null  float64
 5   avg_price_per_room                        36275 non-null  float64
 6   booking_status                            36275 non-null  float64
 7   length_stay                               36275 non-null  float64
 8   no_of_children_log                        36275 non-null  float64
 9   no_of_weekend_nights_log                  36275 non-null  float64
 10  no_of_previous_cancellations_log          36275 non-null  float64
 11  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 12  no_of_special_requests_log                36275 non-null  float64
 13  type_of_meal_plan_Meal Plan 2             36275 non-null  float64
 14  type_of_meal_plan_Meal Plan 3             36275 non-null  float64
 15  type_of_meal_plan_Not Selected            36275 non-null  float64
 16  room_type_reserved_Room_Type 2            36275 non-null  float64
 17  room_type_reserved_Room_Type 3            36275 non-null  float64
 18  room_type_reserved_Room_Type 4            36275 non-null  float64
 19  room_type_reserved_Room_Type 5            36275 non-null  float64
 20  room_type_reserved_Room_Type 6            36275 non-null  float64
 21  room_type_reserved_Room_Type 7            36275 non-null  float64
 22  market_segment_type_Complementary         36275 non-null  float64
 23  market_segment_type_Corporate             36275 non-null  float64
 24  market_segment_type_Offline               36275 non-null  float64
 25  market_segment_type_Online                36275 non-null  float64
 26  lead_time_y_short                         36275 non-null  float64
 27  lead_time_y_med                           36275 non-null  float64
 28  lead_time_y_long                          36275 non-null  float64
 29  lead_time_y_advanced                      36275 non-null  float64
dtypes: float64(30)
memory usage: 8.3 MB


# Using the SCIEM method I will split the train test data first.
X = data4.drop("booking_status" , axis=1)
y = data4.pop("booking_status")

# adding a contstant to X variable
X = add_constant(X)


# Train/Test Split 70/30 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=1)


print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 25392
Number of rows in test data = 10883


print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Percentage of classes in training set:
0.0    0.670644
1.0    0.329356
Name: booking_status, dtype: float64
Percentage of classes in test set:
0.0    0.676376
1.0    0.323624
Name: booking_status, dtype: float64


X_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25392 entries, 13662 to 33003
Data columns (total 30 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   const                                     25392 non-null  float64
 1   no_of_adults                              25392 non-null  float64
 2   no_of_week_nights                         25392 non-null  float64
 3   required_car_parking_space                25392 non-null  float64
 4   arrival_month                             25392 non-null  float64
 5   repeated_guest                            25392 non-null  float64
 6   avg_price_per_room                        25392 non-null  float64
 7   length_stay                               25392 non-null  float64
 8   no_of_children_log                        25392 non-null  float64
 9   no_of_weekend_nights_log                  25392 non-null  float64
 10  no_of_previous_cancellations_log          25392 non-null  float64
 11  no_of_previous_bookings_not_canceled_log  25392 non-null  float64
 12  no_of_special_requests_log                25392 non-null  float64
 13  type_of_meal_plan_Meal Plan 2             25392 non-null  float64
 14  type_of_meal_plan_Meal Plan 3             25392 non-null  float64
 15  type_of_meal_plan_Not Selected            25392 non-null  float64
 16  room_type_reserved_Room_Type 2            25392 non-null  float64
 17  room_type_reserved_Room_Type 3            25392 non-null  float64
 18  room_type_reserved_Room_Type 4            25392 non-null  float64
 19  room_type_reserved_Room_Type 5            25392 non-null  float64
 20  room_type_reserved_Room_Type 6            25392 non-null  float64
 21  room_type_reserved_Room_Type 7            25392 non-null  float64
 22  market_segment_type_Complementary         25392 non-null  float64
 23  market_segment_type_Corporate             25392 non-null  float64
 24  market_segment_type_Offline               25392 non-null  float64
 25  market_segment_type_Online                25392 non-null  float64
 26  lead_time_y_short                         25392 non-null  float64
 27  lead_time_y_med                           25392 non-null  float64
 28  lead_time_y_long                          25392 non-null  float64
 29  lead_time_y_advanced                      25392 non-null  float64
dtypes: float64(30)
memory usage: 6.0 MB


plt.figure(figsize=(20,10))
sns.heatmap(
data4.corr(), annot=True, vmin=-1, vmax=1, fmt='.2f')

<AxesSubplot:>


sns.pairplot(data4[['no_of_adults',
 'required_car_parking_space',
 'arrival_month',
 'repeated_guest',
 'avg_price_per_room',
 'length_stay',
 'no_of_children_log',
 'no_of_previous_cancellations_log',
 'no_of_previous_bookings_not_canceled_log',
 'no_of_special_requests_log',
 'market_segment_type_Complementary',
 'market_segment_type_Corporate',
 'market_segment_type_Offline',
 'market_segment_type_Online',
 'lead_time_y_short',
 'lead_time_y_med',
 'lead_time_y_long',
 'lead_time_y_advanced']]);


# let's check the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                       326.141919
no_of_adults                                  1.346659
no_of_week_nights                           100.277464
required_car_parking_space                    1.041578
arrival_month                                 1.051511
repeated_guest                                3.340040
avg_price_per_room                            1.936037
length_stay                                 146.442538
no_of_children_log                            1.866322
no_of_weekend_nights_log                     34.428764
no_of_previous_cancellations_log              1.597137
no_of_previous_bookings_not_canceled_log      3.508907
no_of_special_requests_log                    1.267959
type_of_meal_plan_Meal Plan 2                 1.217525
type_of_meal_plan_Meal Plan 3                 1.025316
type_of_meal_plan_Not Selected                1.236534
room_type_reserved_Room_Type 2                1.090666
room_type_reserved_Room_Type 3                1.003381
room_type_reserved_Room_Type 4                1.364652
room_type_reserved_Room_Type 5                1.028015
room_type_reserved_Room_Type 6                1.858575
room_type_reserved_Room_Type 7                1.111002
market_segment_type_Complementary             4.507245
market_segment_type_Corporate                16.930019
market_segment_type_Offline                  64.016661
market_segment_type_Online                   71.248287
lead_time_y_short                             1.119269
lead_time_y_med                               1.105320
lead_time_y_long                              1.151626
lead_time_y_advanced                          1.047117
dtype: float64


#dropping the number of weekend & week nights because I have combined them into one & market segements because they all have large multi values
X_train1 = X_train.drop(['no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary'],
                       axis=1)


logit = sm.Logit(y_train, X_train1.astype(float))
lg = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463427
         Iterations 10


# print the logistic regression summary
print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25368
Method:                           MLE   Df Model:                           23
Date:                Fri, 14 Jan 2022   Pseudo R-squ.:                  0.2687
Time:                        18:37:31   Log-Likelihood:                -11767.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
============================================================================================================
                                               coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------------
const                                       -3.6818      0.098    -37.650      0.000      -3.873      -3.490
no_of_adults                                 0.2321      0.035      6.614      0.000       0.163       0.301
required_car_parking_space                  -1.4537      0.135    -10.742      0.000      -1.719      -1.188
arrival_month                               -0.0668      0.006    -11.685      0.000      -0.078      -0.056
repeated_guest                              -2.6424      0.630     -4.193      0.000      -3.878      -1.407
avg_price_per_room                           0.0229      0.001     33.788      0.000       0.022       0.024
length_stay                                  0.1088      0.009     11.946      0.000       0.091       0.127
no_of_children_log                           0.5488      0.093      5.887      0.000       0.366       0.732
no_of_previous_cancellations_log             1.2323      0.490      2.515      0.012       0.272       2.193
no_of_previous_bookings_not_canceled_log    -0.6731      0.477     -1.411      0.158      -1.608       0.262
no_of_special_requests_log                  -1.9180      0.044    -43.892      0.000      -2.004      -1.832
type_of_meal_plan_Meal Plan 2               -0.3480      0.056     -6.165      0.000      -0.459      -0.237
type_of_meal_plan_Meal Plan 3                1.7182      2.912      0.590      0.555      -3.989       7.425
type_of_meal_plan_Not Selected               0.8463      0.048     17.563      0.000       0.752       0.941
room_type_reserved_Room_Type 2               0.1288      0.123      1.045      0.296      -0.113       0.370
room_type_reserved_Room_Type 3              -0.2278      1.194     -0.191      0.849      -2.567       2.111
room_type_reserved_Room_Type 4               0.0548      0.050      1.095      0.273      -0.043       0.153
room_type_reserved_Room_Type 5              -0.9272      0.196     -4.735      0.000      -1.311      -0.543
room_type_reserved_Room_Type 6              -1.0662      0.135     -7.903      0.000      -1.331      -0.802
room_type_reserved_Room_Type 7              -1.8078      0.286     -6.331      0.000      -2.368      -1.248
lead_time_y_short                            1.3167      0.039     34.051      0.000       1.241       1.393
lead_time_y_med                              2.8622      0.058     49.315      0.000       2.748       2.976
lead_time_y_long                             3.0529      0.077     39.428      0.000       2.901       3.205
lead_time_y_advanced                         4.5673      0.247     18.478      0.000       4.083       5.052
============================================================================================================


# let's check the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train1.values, i) for i in range(X_train1.shape[1])],
    index=X_train1.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                       29.389432
no_of_adults                                 1.279036
required_car_parking_space                   1.037343
arrival_month                                1.045906
repeated_guest                               3.216850
avg_price_per_room                           1.583496
length_stay                                  1.076888
no_of_children_log                           1.855492
no_of_previous_cancellations_log             1.576794
no_of_previous_bookings_not_canceled_log     3.445330
no_of_special_requests_log                   1.133817
type_of_meal_plan_Meal Plan 2                1.134531
type_of_meal_plan_Meal Plan 3                1.018636
type_of_meal_plan_Not Selected               1.108819
room_type_reserved_Room_Type 2               1.079517
room_type_reserved_Room_Type 3               1.000877
room_type_reserved_Room_Type 4               1.317003
room_type_reserved_Room_Type 5               1.013508
room_type_reserved_Room_Type 6               1.833828
room_type_reserved_Room_Type 7               1.072268
lead_time_y_short                            1.105340
lead_time_y_med                              1.091932
lead_time_y_long                             1.122973
lead_time_y_advanced                         1.043737
dtype: float64


# test performance
pred_train = lg.predict(X_train1) > 0.5
pred_train = np.round(pred_train)


X_train2 = X_train1.drop(['room_type_reserved_Room_Type 3'], axis=1)
X_train2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25392 entries, 13662 to 33003
Data columns (total 23 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   const                                     25392 non-null  float64
 1   no_of_adults                              25392 non-null  float64
 2   required_car_parking_space                25392 non-null  float64
 3   arrival_month                             25392 non-null  float64
 4   repeated_guest                            25392 non-null  float64
 5   avg_price_per_room                        25392 non-null  float64
 6   length_stay                               25392 non-null  float64
 7   no_of_children_log                        25392 non-null  float64
 8   no_of_previous_cancellations_log          25392 non-null  float64
 9   no_of_previous_bookings_not_canceled_log  25392 non-null  float64
 10  no_of_special_requests_log                25392 non-null  float64
 11  type_of_meal_plan_Meal Plan 2             25392 non-null  float64
 12  type_of_meal_plan_Meal Plan 3             25392 non-null  float64
 13  type_of_meal_plan_Not Selected            25392 non-null  float64
 14  room_type_reserved_Room_Type 2            25392 non-null  float64
 15  room_type_reserved_Room_Type 4            25392 non-null  float64
 16  room_type_reserved_Room_Type 5            25392 non-null  float64
 17  room_type_reserved_Room_Type 6            25392 non-null  float64
 18  room_type_reserved_Room_Type 7            25392 non-null  float64
 19  lead_time_y_short                         25392 non-null  float64
 20  lead_time_y_med                           25392 non-null  float64
 21  lead_time_y_long                          25392 non-null  float64
 22  lead_time_y_advanced                      25392 non-null  float64
dtypes: float64(23)
memory usage: 4.6 MB


logit = sm.Logit(y_train, X_train2.astype(float))
lg2 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463428
         Iterations 10


print(lg2.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25369
Method:                           MLE   Df Model:                           22
Date:                Fri, 14 Jan 2022   Pseudo R-squ.:                  0.2687
Time:                        18:37:34   Log-Likelihood:                -11767.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
============================================================================================================
                                               coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------------
const                                       -3.6818      0.098    -37.651      0.000      -3.873      -3.490
no_of_adults                                 0.2321      0.035      6.614      0.000       0.163       0.301
required_car_parking_space                  -1.4536      0.135    -10.742      0.000      -1.719      -1.188
arrival_month                               -0.0668      0.006    -11.687      0.000      -0.078      -0.056
repeated_guest                              -2.6423      0.630     -4.193      0.000      -3.878      -1.407
avg_price_per_room                           0.0229      0.001     33.789      0.000       0.022       0.024
length_stay                                  0.1089      0.009     11.948      0.000       0.091       0.127
no_of_children_log                           0.5489      0.093      5.887      0.000       0.366       0.732
no_of_previous_cancellations_log             1.2322      0.490      2.515      0.012       0.272       2.193
no_of_previous_bookings_not_canceled_log    -0.6731      0.477     -1.411      0.158      -1.608       0.262
no_of_special_requests_log                  -1.9180      0.044    -43.892      0.000      -2.004      -1.832
type_of_meal_plan_Meal Plan 2               -0.3479      0.056     -6.163      0.000      -0.459      -0.237
type_of_meal_plan_Meal Plan 3                1.7183      2.912      0.590      0.555      -3.988       7.425
type_of_meal_plan_Not Selected               0.8463      0.048     17.564      0.000       0.752       0.941
room_type_reserved_Room_Type 2               0.1289      0.123      1.046      0.296      -0.113       0.371
room_type_reserved_Room_Type 4               0.0549      0.050      1.097      0.273      -0.043       0.153
room_type_reserved_Room_Type 5              -0.9271      0.196     -4.735      0.000      -1.311      -0.543
room_type_reserved_Room_Type 6              -1.0662      0.135     -7.903      0.000      -1.331      -0.802
room_type_reserved_Room_Type 7              -1.8078      0.286     -6.331      0.000      -2.367      -1.248
lead_time_y_short                            1.3166      0.039     34.051      0.000       1.241       1.392
lead_time_y_med                              2.8622      0.058     49.315      0.000       2.748       2.976
lead_time_y_long                             3.0529      0.077     39.429      0.000       2.901       3.205
lead_time_y_advanced                         4.5673      0.247     18.478      0.000       4.083       5.052
============================================================================================================


X_train3 = X_train2.drop(['no_of_previous_bookings_not_canceled_log'], axis=1)


logit = sm.Logit(y_train, X_train3.astype(float))
lg3 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463479
         Iterations 9


print(lg3.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25370
Method:                           MLE   Df Model:                           21
Date:                Fri, 14 Jan 2022   Pseudo R-squ.:                  0.2686
Time:                        18:37:34   Log-Likelihood:                -11769.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
const                               -3.6853      0.098    -37.686      0.000      -3.877      -3.494
no_of_adults                         0.2328      0.035      6.633      0.000       0.164       0.302
required_car_parking_space          -1.4531      0.135    -10.738      0.000      -1.718      -1.188
arrival_month                       -0.0667      0.006    -11.679      0.000      -0.078      -0.056
repeated_guest                      -2.9666      0.574     -5.169      0.000      -4.092      -1.842
avg_price_per_room                   0.0229      0.001     33.812      0.000       0.022       0.024
length_stay                          0.1089      0.009     11.954      0.000       0.091       0.127
no_of_children_log                   0.5492      0.093      5.890      0.000       0.366       0.732
no_of_previous_cancellations_log     0.9583      0.399      2.401      0.016       0.176       1.741
no_of_special_requests_log          -1.9193      0.044    -43.924      0.000      -2.005      -1.834
type_of_meal_plan_Meal Plan 2       -0.3491      0.056     -6.183      0.000      -0.460      -0.238
type_of_meal_plan_Meal Plan 3        1.7180      2.914      0.590      0.555      -3.992       7.429
type_of_meal_plan_Not Selected       0.8466      0.048     17.568      0.000       0.752       0.941
room_type_reserved_Room_Type 2       0.1290      0.123      1.046      0.296      -0.113       0.371
room_type_reserved_Room_Type 4       0.0543      0.050      1.085      0.278      -0.044       0.152
room_type_reserved_Room_Type 5      -0.9291      0.196     -4.746      0.000      -1.313      -0.545
room_type_reserved_Room_Type 6      -1.0676      0.135     -7.913      0.000      -1.332      -0.803
room_type_reserved_Room_Type 7      -1.8098      0.286     -6.337      0.000      -2.370      -1.250
lead_time_y_short                    1.3170      0.039     34.060      0.000       1.241       1.393
lead_time_y_med                      2.8638      0.058     49.342      0.000       2.750       2.978
lead_time_y_long                     3.0543      0.077     39.438      0.000       2.903       3.206
lead_time_y_advanced                 4.5896      0.248     18.494      0.000       4.103       5.076
====================================================================================================


# let's check the VIF of the predictors again to see if any Multicollinearity persist
vif_series = pd.Series(
    [variance_inflation_factor(X_train3.values, i) for i in range(X_train3.shape[1])],
    index=X_train3.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                               29.239515
no_of_adults                         1.276452
required_car_parking_space           1.036585
arrival_month                        1.044675
repeated_guest                       1.552463
avg_price_per_room                   1.578995
length_stay                          1.076638
no_of_children_log                   1.855384
no_of_previous_cancellations_log     1.426094
no_of_special_requests_log           1.128277
type_of_meal_plan_Meal Plan 2        1.134291
type_of_meal_plan_Meal Plan 3        1.018576
type_of_meal_plan_Not Selected       1.108682
room_type_reserved_Room_Type 2       1.079477
room_type_reserved_Room_Type 4       1.316395
room_type_reserved_Room_Type 5       1.012609
room_type_reserved_Room_Type 6       1.832724
room_type_reserved_Room_Type 7       1.071514
lead_time_y_short                    1.105261
lead_time_y_med                      1.091919
lead_time_y_long                     1.122907
lead_time_y_advanced                 1.043512
dtype: float64


X_train4 = X_train3.drop(['room_type_reserved_Room_Type 2'], axis=1)


logit = sm.Logit(y_train, X_train4.astype(float))
lg4 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463500
         Iterations 9


print(lg4.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25371
Method:                           MLE   Df Model:                           20
Date:                Fri, 14 Jan 2022   Pseudo R-squ.:                  0.2686
Time:                        18:37:36   Log-Likelihood:                -11769.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
const                               -3.6755      0.097    -37.773      0.000      -3.866      -3.485
no_of_adults                         0.2313      0.035      6.592      0.000       0.162       0.300
required_car_parking_space          -1.4496      0.135    -10.723      0.000      -1.715      -1.185
arrival_month                       -0.0669      0.006    -11.722      0.000      -0.078      -0.056
repeated_guest                      -2.9693      0.574     -5.173      0.000      -4.094      -1.844
avg_price_per_room                   0.0229      0.001     33.816      0.000       0.022       0.024
length_stay                          0.1090      0.009     11.964      0.000       0.091       0.127
no_of_children_log                   0.5688      0.091      6.225      0.000       0.390       0.748
no_of_previous_cancellations_log     0.9574      0.399      2.398      0.016       0.175       1.740
no_of_special_requests_log          -1.9178      0.044    -43.917      0.000      -2.003      -1.832
type_of_meal_plan_Meal Plan 2       -0.3510      0.056     -6.220      0.000      -0.462      -0.240
type_of_meal_plan_Meal Plan 3        1.7193      2.911      0.591      0.555      -3.987       7.425
type_of_meal_plan_Not Selected       0.8443      0.048     17.541      0.000       0.750       0.939
room_type_reserved_Room_Type 4       0.0528      0.050      1.056      0.291      -0.045       0.151
room_type_reserved_Room_Type 5      -0.9320      0.196     -4.761      0.000      -1.316      -0.548
room_type_reserved_Room_Type 6      -1.0857      0.134     -8.110      0.000      -1.348      -0.823
room_type_reserved_Room_Type 7      -1.8191      0.286     -6.369      0.000      -2.379      -1.259
lead_time_y_short                    1.3172      0.039     34.067      0.000       1.241       1.393
lead_time_y_med                      2.8676      0.058     49.483      0.000       2.754       2.981
lead_time_y_long                     3.0549      0.077     39.444      0.000       2.903       3.207
lead_time_y_advanced                 4.5912      0.248     18.498      0.000       4.105       5.078
====================================================================================================


X_train5 = X_train4.drop(['room_type_reserved_Room_Type 4'], axis=1)


logit = sm.Logit(y_train, X_train5.astype(float))
lg5 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463522
         Iterations 9


print(lg5.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25372
Method:                           MLE   Df Model:                           19
Date:                Fri, 14 Jan 2022   Pseudo R-squ.:                  0.2686
Time:                        18:37:36   Log-Likelihood:                -11770.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
const                               -3.7035      0.094    -39.515      0.000      -3.887      -3.520
no_of_adults                         0.2398      0.034      7.023      0.000       0.173       0.307
required_car_parking_space          -1.4500      0.135    -10.728      0.000      -1.715      -1.185
arrival_month                       -0.0672      0.006    -11.767      0.000      -0.078      -0.056
repeated_guest                      -2.9647      0.574     -5.163      0.000      -4.090      -1.839
avg_price_per_room                   0.0231      0.001     35.959      0.000       0.022       0.024
length_stay                          0.1100      0.009     12.154      0.000       0.092       0.128
no_of_children_log                   0.5594      0.091      6.150      0.000       0.381       0.738
no_of_previous_cancellations_log     0.9559      0.400      2.392      0.017       0.173       1.739
no_of_special_requests_log          -1.9157      0.044    -43.926      0.000      -2.001      -1.830
type_of_meal_plan_Meal Plan 2       -0.3601      0.056     -6.456      0.000      -0.469      -0.251
type_of_meal_plan_Meal Plan 3        1.7283      2.969      0.582      0.560      -4.090       7.547
type_of_meal_plan_Not Selected       0.8335      0.047     17.728      0.000       0.741       0.926
room_type_reserved_Room_Type 5      -0.9481      0.195     -4.858      0.000      -1.331      -0.566
room_type_reserved_Room_Type 6      -1.1095      0.132     -8.409      0.000      -1.368      -0.851
room_type_reserved_Room_Type 7      -1.8578      0.283     -6.556      0.000      -2.413      -1.302
lead_time_y_short                    1.3157      0.039     34.057      0.000       1.240       1.391
lead_time_y_med                      2.8630      0.058     49.547      0.000       2.750       2.976
lead_time_y_long                     3.0495      0.077     39.450      0.000       2.898       3.201
lead_time_y_advanced                 4.5867      0.248     18.479      0.000       4.100       5.073
====================================================================================================


# converting coefficients to odds
odds = np.exp(lg5.params)

# adding the odds to a dataframe
pd.DataFrame(odds, X_train5.columns, columns=["odds"]).T


# finding the percentage change
perc_change_odds = (np.exp(lg5.params) - 1) * 100

# adding the change_odds% to a dataframe
pd.DataFrame(perc_change_odds, X_train3.columns, columns=["change_odds%"]).T


# fitting the model on training set
logit = sm.Logit(y_train, X_train5.astype(float))
lg3 = logit.fit()

pred_train4 = lg5.predict(X_train5)
pred_train4 = np.round(pred_train4)

Optimization terminated successfully.
         Current function value: 0.463522
         Iterations 9


# another confusion matrix
cm = confusion_matrix(y_train, pred_train4)
plt.figure(figsize=(7, 5))
sns.heatmap(cm, annot=True, fmt="g")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.show()


print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))

Accuracy on training set :  0.7811515437933207


logit_roc_auc_train = roc_auc_score(y_train, lg5.predict(X_train5))
fpr, tpr, thresholds = roc_curve(y_train, lg5.predict(X_train5))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# dropping variables from test set as well which were dropped from training set
X_test1 = X_test.drop([ 'no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary',
                       'room_type_reserved_Room_Type 3',
                       'room_type_reserved_Room_Type 4',
                       'no_of_previous_bookings_not_canceled_log',
                       'room_type_reserved_Room_Type 2'
                      
                     ], axis=1)


pred_test = lg5.predict(X_test1) > 0.5
pred_test = np.round(pred_test)
print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))
print("Accuracy on test set : ", accuracy_score(y_test, pred_test))

Accuracy on training set :  0.7811515437933207
Accuracy on test set :  0.7846182118901038


tree_data = dummy_data.astype(float)


tree_data = tree_data.drop(['arrival_date','arrival_year','no_of_week_nights',
'no_of_weekend_nights_log'  ], axis=1)


X = tree_data.drop("booking_status" , axis=1)
y = tree_data.pop("booking_status")


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=1)


# building a decision tree using the dtclassifier function
dTree = DecisionTreeClassifier(criterion = 'gini', random_state=1)
dTree.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)


#scoring the accuracy on train & test data
print("Accuracy on training set : ",dTree.score(X_train, y_train))
print("Accuracy on test set : ",dTree.score(X_test, y_test))

Accuracy on training set :  0.9924385633270322
Accuracy on test set :  0.8585867867315997


# checking the positive outcomes
y.sum(axis = 0)

11885.0


## Function to create confusion matrix
def make_confusion_matrix(model,y_actual,labels=[1, 0]):
    '''
    model : classifier to predict values of X
    y_actual : ground truth  
    
    '''
    y_predict = model.predict(X_test)
    cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
    df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                  columns = [i for i in ['Predicted - No','Predicted - Yes']])
    group_counts = ["{0:0.0f}".format(value) for value in
                cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in
                         cm.flatten()/np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in
              zip(group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    plt.figure(figsize = (10,7))
    sns.heatmap(df_cm, annot=labels,fmt='')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


##  Function to calculate recall score
def get_recall_score(model):
    '''
    model : classifier to predict values of X

    '''
    pred_train = model.predict(X_train)
    pred_test = model.predict(X_test)
    print("Recall on training set : ",metrics.recall_score(y_train,pred_train))
    print("Recall on test set : ",metrics.recall_score(y_test,pred_test))


# another confusion matrix
make_confusion_matrix(dTree,y_test)


# check the recall on the train and test.
get_recall_score(dTree)

Recall on training set :  0.9817051297381323
Recall on test set :  0.7921635434412265


the_features = list(X.columns)
print(the_features)

['no_of_adults', 'required_car_parking_space', 'lead_time', 'arrival_month', 'repeated_guest', 'avg_price_per_room', 'length_stay', 'no_of_children_log', 'no_of_previous_cancellations_log', 'no_of_previous_bookings_not_canceled_log', 'no_of_special_requests_log', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Complementary', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'market_segment_type_Online']


plt.figure(figsize=(20,30))
tree.plot_tree(dTree,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


print(tree.export_text(dTree,feature_names=the_features,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests_log <= 0.35
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |--- avg_price_per_room <= 201.50
|   |   |   |   |   |   |--- lead_time <= 74.50
|   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 57.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  57.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 151.59
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  151.59
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [169.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 50.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  50.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |--- lead_time >  74.50
|   |   |   |   |   |   |   |--- lead_time <= 78.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.47
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [26.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.47
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 30.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  78.50
|   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [110.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 86.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  86.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.75
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 63.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  63.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.75
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 73.53
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  73.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |--- avg_price_per_room >  201.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1.0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |--- avg_price_per_room <= 115.50
|   |   |   |   |   |   |--- length_stay <= 14.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 75.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  75.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.42
|   |   |   |   |   |   |   |   |   |--- weights: [34.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.42
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.42
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- length_stay >  14.50
|   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |--- avg_price_per_room >  115.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- length_stay <= 10.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 43.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  10.00
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- avg_price_per_room <= 75.07
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 104.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  104.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  75.07
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [50.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 86.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  86.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.50
|   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 59.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 101.12
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  101.12
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 47.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 177.83
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [45.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  177.83
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 110.86
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  110.86
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |--- weights: [141.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 123.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 82.88
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  82.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  123.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 122.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 63.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  63.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  122.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [65.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.75
|   |   |   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 138.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  138.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.75
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.41
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.41
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [60.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 202.67
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [67.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |--- length_stay <= 12.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.35
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.40
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.40
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.35
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  12.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.38
|   |   |   |   |   |   |   |--- avg_price_per_room <= 78.90
|   |   |   |   |   |   |   |   |--- length_stay <= 15.00
|   |   |   |   |   |   |   |   |   |--- length_stay <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [84.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  15.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  78.90
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [42.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  99.38
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.25
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.25
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 160.17
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [41.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  160.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  202.67
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [0.00, 32.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- avg_price_per_room <= 105.27
|   |   |   |   |   |--- avg_price_per_room <= 60.07
|   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |--- lead_time <= 51.50
|   |   |   |   |   |   |   |   |--- lead_time <= 50.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 21.67
|   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  21.67
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 49.84
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  49.84
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time >  50.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  51.50
|   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |--- lead_time <= 87.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  87.50
|   |   |   |   |   |   |   |   |--- length_stay <= 8.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.43
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.43
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  8.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- avg_price_per_room >  60.07
|   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [29.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [54.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 35.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  105.27
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 195.30
|   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 38.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  38.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |--- avg_price_per_room >  195.30
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 92.00] class: 1.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- lead_time <= 22.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  22.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 168.06
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 147.75
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  147.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  168.06
|   |   |   |   |   |   |   |   |   |--- length_stay <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 80.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  80.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- length_stay <= 11.00
|   |   |   |   |   |   |   |--- weights: [39.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  11.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |--- no_of_special_requests_log >  0.35
|   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |   |--- length_stay <= 15.00
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [848.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [27.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.35
|   |   |   |   |   |   |   |   |   |   |--- weights: [43.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_children_log >  0.35
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 35.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  35.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  15.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |   |--- lead_time <= 104.50
|   |   |   |   |   |   |   |--- lead_time <= 103.50
|   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.35
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_children_log >  0.35
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  103.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  104.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 141.75
|   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [27.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  141.75
|   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |--- lead_time <= 63.00
|   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |--- weights: [18.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  63.00
|   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- length_stay <= 14.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 219.86
|   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.64
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.64
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 158.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  158.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  219.86
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 237.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  237.25
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- length_stay >  14.00
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 123.60
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.76
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.76
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- weights: [95.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  123.60
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.05
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.05
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 128.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  128.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 127.62
|   |   |   |   |   |   |   |--- lead_time <= 43.50
|   |   |   |   |   |   |   |   |--- length_stay <= 9.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [87.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [127.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.22
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  29.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  43.50
|   |   |   |   |   |   |   |   |--- length_stay <= 10.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.54
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.54
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- length_stay >  10.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |--- avg_price_per_room >  127.62
|   |   |   |   |   |   |   |--- lead_time <= 142.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 179.62
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  179.62
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 139.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  139.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [49.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- lead_time >  142.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 142.65
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.00
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  142.65
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 182.49
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  182.49
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |--- weights: [180.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.90
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- length_stay <= 12.00
|   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- weights: [1689.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.05
|   |   |   |   |   |   |   |   |   |--- lead_time <= 48.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [61.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  48.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.05
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [221.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 31.00
|   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  31.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 159.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  159.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.33
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.95
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.95
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.33
|   |   |   |   |   |   |   |   |   |--- lead_time <= 80.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  80.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |--- lead_time <= 9.00
|   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  9.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 34.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.24
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time >  34.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 72.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  72.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |--- weights: [69.00, 0.00] class: 0.0
|   |   |   |   |--- length_stay >  12.00
|   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- avg_price_per_room <= 202.95
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.33
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.37
|   |   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.37
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 98.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  98.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.33
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 115.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  115.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.35
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 142.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  142.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_children_log >  0.35
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 105.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  105.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 103.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  103.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |--- avg_price_per_room <= 90.42
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 107.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.52
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.52
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  107.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 101.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  101.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- avg_price_per_room >  90.42
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 153.15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.60
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.60
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  153.15
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |--- weights: [52.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  202.95
|   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests_log <= 0.35
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1.0
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- lead_time <= 341.00
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [61.00, 6.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 231.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  231.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  341.00
|   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 402.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 381.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  381.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 2.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  402.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.33
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |--- avg_price_per_room <= 84.58
|   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 166.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  166.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 229.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.34
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.34
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  229.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 27.07
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  27.07
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.83
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.83
|   |   |   |   |   |   |   |   |   |--- lead_time <= 292.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  292.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 23.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [37.00, 0.00] class: 0.0
|   |   |   |   |   |--- avg_price_per_room >  84.58
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 316.00
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  316.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 338.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  338.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 5.00] class: 1.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- lead_time <= 285.50
|   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  285.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [0.00, 525.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- lead_time <= 204.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  204.00
|   |   |   |   |   |   |   |   |--- lead_time <= 214.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  214.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 275.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  275.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 80.51
|   |   |   |   |   |   |   |   |--- weights: [0.00, 41.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  80.51
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.43
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.43
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 13.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.35
|   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- lead_time <= 152.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.81
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.81
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  152.50
|   |   |   |   |   |   |   |   |--- lead_time <= 156.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  156.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 87.12
|   |   |   |   |   |   |   |   |--- lead_time <= 158.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  158.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  87.12
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.75
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.75
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- no_of_adults <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- no_of_adults >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 93.44
|   |   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 162.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  162.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [52.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.38
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.92
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.92
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  93.44
|   |   |   |   |   |   |   |   |--- lead_time <= 178.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 93.67
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  93.67
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  178.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 179.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  179.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 1.00] class: 0.0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |--- lead_time <= 187.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.30
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.30
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 20.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  187.50
|   |   |   |   |   |   |   |   |--- lead_time <= 304.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.90
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 237.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  237.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.90
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |--- lead_time >  304.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1.0
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |--- length_stay <= 13.50
|   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.32
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 247.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  247.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.32
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.89
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.89
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  13.50
|   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1.0
|   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |--- lead_time <= 368.00
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- length_stay <= 7.50
|   |   |   |   |   |   |   |   |--- lead_time <= 331.00
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |   |   |   |   |   |   |   |--- weights: [137.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  0.90
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  331.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 336.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  336.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.74
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.74
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- lead_time <= 196.00
|   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  196.00
|   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  0.90
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |--- weights: [6.00, 2.00] class: 0.0
|   |   |   |   |--- lead_time >  368.00
|   |   |   |   |   |--- lead_time <= 381.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  381.50
|   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |--- weights: [0.00, 2108.00] class: 1.0
|   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |--- weights: [31.00, 0.00] class: 0.0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests_log <= 0.35
|   |   |   |   |--- weights: [47.00, 0.00] class: 0.0
|   |   |   |--- no_of_special_requests_log >  0.35
|   |   |   |   |--- lead_time <= 289.50
|   |   |   |   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |   |   |   |--- avg_price_per_room <= 114.59
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  114.59
|   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |--- no_of_special_requests_log >  0.90
|   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 110.46
|   |   |   |   |   |   |   |   |--- lead_time <= 206.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  206.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  110.46
|   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |--- lead_time >  289.50
|   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0


# checking out what variables are being prioritized by the model.

print (pd.DataFrame(dTree.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                               Imp
lead_time                                 0.397083
avg_price_per_room                        0.207281
market_segment_type_Online                0.092752
arrival_month                             0.084426
length_stay                               0.073260
no_of_special_requests_log                0.068314
no_of_adults                              0.029704
type_of_meal_plan_Not Selected            0.011106
room_type_reserved_Room_Type 4            0.008201
required_car_parking_space                0.007376
no_of_children_log                        0.005896
type_of_meal_plan_Meal Plan 2             0.004562
market_segment_type_Offline               0.003515
room_type_reserved_Room_Type 2            0.002241
room_type_reserved_Room_Type 5            0.001711
room_type_reserved_Room_Type 6            0.000748
market_segment_type_Corporate             0.000693
repeated_guest                            0.000471
room_type_reserved_Room_Type 7            0.000337
no_of_previous_cancellations_log          0.000323
room_type_reserved_Room_Type 3            0.000000
market_segment_type_Complementary         0.000000
no_of_previous_bookings_not_canceled_log  0.000000
type_of_meal_plan_Meal Plan 3             0.000000


importances = dTree.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


# Pre prune the model with max depth hyperparameter
dTree1 = DecisionTreeClassifier(criterion = 'gini',max_depth=3,random_state=1)
dTree1.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, random_state=1)


# another confusion matrix
make_confusion_matrix(dTree1, y_test)


# The accuracy on the pre pruned tree.
print("Accuracy on training set : ",dTree1.score(X_train, y_train))
print("Accuracy on test set : ",dTree1.score(X_test, y_test))
# Check the recall with the get_recall_score user defined function
get_recall_score(dTree1)

Accuracy on training set :  0.7844202898550725
Accuracy on test set :  0.7913259211614444
Recall on training set :  0.7315556618438359
Recall on test set :  0.7385008517887564


# Let's see the pre pruned tree
plt.figure(figsize=(15,10))

tree.plot_tree(dTree1,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


print(tree.export_text(dTree1,feature_names=the_features,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests_log <= 0.35
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- weights: [4614.00, 781.00] class: 0.0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- weights: [2504.00, 2768.00] class: 1.0
|   |--- no_of_special_requests_log >  0.35
|   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |--- weights: [5624.00, 1055.00] class: 0.0
|   |   |--- no_of_special_requests_log >  0.90
|   |   |   |--- weights: [2919.00, 145.00] class: 0.0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests_log <= 0.35
|   |   |   |--- weights: [694.00, 1242.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.35
|   |   |   |--- weights: [586.00, 249.00] class: 0.0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- weights: [31.00, 2108.00] class: 1.0
|   |   |--- arrival_month >  11.50
|   |   |   |--- weights: [57.00, 15.00] class: 0.0


# Looking at the feature importances of this model
importances = dTree1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10,10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


# Choose the type of classifier. 
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from
## add from article
parameters = {'max_depth': np.arange(1,10), 
              'min_samples_leaf': [1, 2, 5, 7, 10,15,20],
              'max_leaf_nodes' : [2, 3, 5, 10],
              'min_impurity_decrease': [0.001,0.01,0.1]
             }

# scoring function used to compare parameter combinations
acc_scorer = metrics.make_scorer(metrics.recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data. 
estimator.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, max_leaf_nodes=5,
                       min_impurity_decrease=0.001, random_state=1)


# run the estimator in a confusion matrix
make_confusion_matrix(estimator,y_test)


# The accuracy on the estimator tree.
print("Accuracy on training set : ",estimator.score(X_train, y_train))
print("Accuracy on test set : ",estimator.score(X_test, y_test))
# Check the recall with the get_recall_score user defined function
get_recall_score(dTree1)

Accuracy on training set :  0.7694943289224953
Accuracy on test set :  0.7719378847744188
Recall on training set :  0.7315556618438359
Recall on test set :  0.7385008517887564


plt.figure(figsize=(15,10))

tree.plot_tree(estimator,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


importances = estimator.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


clf = DecisionTreeClassifier(random_state=1)
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities


pd.DataFrame(path)


fig, ax = plt.subplots(figsize=(10,5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()


#Decisiion Tree calssifier for every alpha 
clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
    clf.fit(X_train, y_train)
    clfs.append(clf)
print("Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
      clfs[-1].tree_.node_count, ccp_alphas[-1]))

Number of nodes in the last tree is: 1 with ccp_alpha: 0.0765778947737134


clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1,figsize=(10,7))
ax[0].plot(ccp_alphas, node_counts, marker='o', drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker='o', drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()


train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]


fig, ax = plt.subplots(figsize=(10,5))
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_scores, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, test_scores, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()


index_best_model = np.argmax(test_scores)
best_model = clfs[index_best_model]
print(best_model)
print('Training accuracy of best model: ',best_model.score(X_train, y_train))
print('Test accuracy of best model: ',best_model.score(X_test, y_test))

DecisionTreeClassifier(ccp_alpha=9.904212140385933e-05, random_state=1)
Training accuracy of best model:  0.90544265910523
Test accuracy of best model:  0.8783423688321235


recall_train=[]
for clf in clfs:
    pred_train3=clf.predict(X_train)
    values_train=metrics.recall_score(y_train,pred_train3)
    recall_train.append(values_train)


recall_test=[]
for clf in clfs:
    pred_test3=clf.predict(X_test)
    values_test=metrics.recall_score(y_test,pred_test3)
    recall_test.append(values_test)


fig, ax = plt.subplots(figsize=(15,5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()


# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=2.450465588461807e-05, random_state=1)


#another confusion matrix
make_confusion_matrix(best_model,y_test)


# Recall on train and test
get_recall_score(best_model)

Recall on training set :  0.9790744947985173
Recall on test set :  0.7935831913685406


# plot the additional tree
plt.figure(figsize=(17,15))

tree.plot_tree(best_model,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


# showing what metrics this model used
importances = best_model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


comparison_frame = pd.DataFrame({'Model':['Initial decision tree model','Decision tree with restricted maximum depth','Decision treee with hyperparameter tuning',
                                         'Decision tree with post-pruning'], 'Train_Recall':[.981,.732,.732,.979], 'Test_Recall':[.792,.739,.739,.794]}) 
comparison_frame

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests
count	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000
mean	1.844962	0.105279	0.810724	2.204300	0.030986	85.232557	2017.820427	7.423653	15.596995	0.025637	0.023349	0.153411	103.423539	0.619655
std	0.518715	0.402648	0.870644	1.410905	0.173281	85.930817	0.383836	3.069894	8.740447	0.158053	0.368331	1.754171	35.089424	0.786236
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2017.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2.000000	0.000000	0.000000	1.000000	0.000000	17.000000	2018.000000	5.000000	8.000000	0.000000	0.000000	0.000000	80.300000	0.000000
50%	2.000000	0.000000	1.000000	2.000000	0.000000	57.000000	2018.000000	8.000000	16.000000	0.000000	0.000000	0.000000	99.450000	0.000000
75%	2.000000	0.000000	2.000000	3.000000	0.000000	126.000000	2018.000000	10.000000	23.000000	0.000000	0.000000	0.000000	120.000000	1.000000
max	4.000000	10.000000	7.000000	17.000000	1.000000	443.000000	2018.000000	12.000000	31.000000	1.000000	13.000000	58.000000	540.000000	5.000000

	ccp_alphas	impurities
0	0.000000e+00	0.009478
1	0.000000e+00	0.009478
2	0.000000e+00	0.009478
3	4.688391e-07	0.009478
4	5.329960e-07	0.009479
...	...	...
1508	6.665684e-03	0.286897
1509	1.304480e-02	0.299942
1510	1.725993e-02	0.317202
1511	2.399048e-02	0.365183
1512	7.657789e-02	0.441761

INN Hotels Project¶

Context¶

Objective¶

Data Description¶

Define The Problem¶

1) Predicting which booking is likely to be canceled.¶

2) Identifying factors that have a high influence on booking cancellations.¶

3) Formulate reccomendations for reducing cancellations, or reducing sunk cost resulting from cancellations.¶

Importing necessary libraries and data¶

Data Overview¶

Take a first look at the data¶

Observations¶

Exploratory Data Analysis (EDA)¶

Data Preprocessing¶

EDA¶

EDA Insights¶

The client has a robust pricing structure.¶

Checking Multicollinearity¶

Building a Logistic Regression model¶

Model performance evaluation¶

Final Model Summary¶

Building a Decision Tree model¶

Prune the Model¶

Let's use GridSearch to hyperparameter tune the model¶

Cost Complexity Pruning¶

Actionable Insights and Recommendations¶

My Recommendations¶

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50	0	Canceled

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
36270	INN36271	3	2	6	Meal Plan 1	Room_Type 4	85	2018	8	3	Online	167.80	1	Not_Canceled
36271	INN36272	2	1	3	Meal Plan 1	Room_Type 1	228	2018	10	17	Online	90.95	2	Canceled
36272	INN36273	2	2	6	Meal Plan 1	Room_Type 1	148	2018	7	1	Online	98.39	2	Not_Canceled
36273	INN36274	2	0	3	Not Selected	Room_Type 1	63	2018	4	21	Online	94.50	0	Canceled
36274	INN36275	2	1	2	Meal Plan 1	Room_Type 1	207	2018	12	30	Offline	161.67	0	Not_Canceled

	Booking_ID	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
30392	INN30393	1	0	1	0	Not Selected	0	Room_Type 1	53	2018	9	11	Online	0	0	0	94.32	0	Not_Canceled
6685	INN06686	2	0	1	2	Meal Plan 1	0	Room_Type 1	63	2018	4	22	Online	0	0	0	105.30	1	Canceled
8369	INN08370	2	0	2	3	Meal Plan 1	0	Room_Type 4	55	2018	9	11	Online	0	0	0	106.24	0	Not_Canceled
2055	INN02056	2	0	0	2	Not Selected	0	Room_Type 1	53	2017	12	29	Online	0	0	0	81.00	1	Not_Canceled
10969	INN10970	1	0	2	4	Meal Plan 1	0	Room_Type 1	245	2018	7	6	Offline	0	0	0	110.00	0	Canceled
24881	INN24882	2	0	3	7	Meal Plan 1	0	Room_Type 2	231	2018	8	1	Online	0	0	0	81.82	2	Canceled
28658	INN28659	2	0	0	3	Meal Plan 2	0	Room_Type 1	71	2018	5	10	Offline	0	0	0	126.00	1	Not_Canceled
20853	INN20854	2	0	1	2	Meal Plan 1	0	Room_Type 1	66	2017	10	9	Offline	0	0	0	75.00	0	Canceled
8501	INN08502	2	0	0	3	Meal Plan 1	0	Room_Type 2	40	2018	1	14	Online	0	0	0	77.55	1	Not_Canceled
1942	INN01943	2	0	0	2	Meal Plan 1	0	Room_Type 1	63	2018	8	9	Online	0	0	0	144.90	2	Not_Canceled
15648	INN15649	2	0	2	4	Meal Plan 1	0	Room_Type 1	209	2018	7	2	Online	0	0	0	66.53	1	Not_Canceled
6116	INN06117	2	0	0	1	Meal Plan 1	0	Room_Type 1	9	2018	7	6	Online	0	0	0	139.00	1	Not_Canceled
7868	INN07869	2	0	2	4	Meal Plan 1	0	Room_Type 4	123	2018	5	22	Online	0	0	0	114.75	1	Canceled
24527	INN24528	2	0	0	3	Meal Plan 1	0	Room_Type 1	118	2018	6	28	Online	0	0	0	96.30	0	Canceled
24227	INN24228	2	0	1	0	Meal Plan 1	0	Room_Type 4	72	2018	10	9	Online	0	0	0	132.30	3	Not_Canceled
17216	INN17217	2	0	0	1	Meal Plan 1	0	Room_Type 1	1	2018	11	23	Online	0	0	0	120.00	0	Not_Canceled
31124	INN31125	1	0	0	1	Meal Plan 1	0	Room_Type 1	7	2017	8	28	Corporate	1	1	2	65.00	0	Not_Canceled
9101	INN09102	1	0	2	1	Meal Plan 1	0	Room_Type 1	12	2018	10	2	Online	0	0	0	6.00	0	Not_Canceled
9474	INN09475	2	0	0	2	Meal Plan 2	0	Room_Type 1	63	2017	9	4	Offline	0	0	0	116.00	0	Not_Canceled
12782	INN12783	2	0	0	3	Not Selected	0	Room_Type 1	18	2018	1	21	Online	0	0	0	77.00	1	Not_Canceled
28297	INN28298	1	0	2	5	Meal Plan 1	0	Room_Type 1	68	2018	8	29	Online	0	0	0	92.35	1	Not_Canceled
22021	INN22022	2	0	0	2	Meal Plan 1	0	Room_Type 1	42	2018	11	4	Offline	0	0	0	72.00	0	Not_Canceled
26586	INN26587	3	0	2	2	Meal Plan 1	0	Room_Type 4	53	2018	3	20	Online	0	0	0	124.10	1	Canceled
16756	INN16757	2	0	1	2	Meal Plan 1	0	Room_Type 1	305	2018	11	4	Offline	0	0	0	89.00	0	Canceled
20927	INN20928	2	0	2	6	Meal Plan 1	0	Room_Type 1	106	2018	7	8	Offline	0	0	0	72.25	2	Not_Canceled
35753	INN35754	2	0	1	2	Meal Plan 1	0	Room_Type 1	128	2018	6	20	Online	0	0	0	120.00	0	Canceled
9359	INN09360	1	0	1	0	Meal Plan 1	0	Room_Type 1	7	2018	5	16	Online	0	0	0	97.00	1	Not_Canceled
21929	INN21930	1	0	0	4	Meal Plan 1	0	Room_Type 4	48	2018	8	24	Online	0	0	0	149.40	1	Not_Canceled
17501	INN17502	2	0	0	3	Not Selected	0	Room_Type 1	27	2018	3	22	Online	0	0	0	129.00	0	Canceled
3355	INN03356	2	1	2	2	Meal Plan 1	0	Room_Type 1	173	2018	8	13	Online	0	0	0	114.75	1	Canceled
22183	INN22184	1	0	0	1	Meal Plan 1	0	Room_Type 1	9	2018	6	1	Online	0	0	0	97.02	1	Not_Canceled
7818	INN07819	2	0	0	1	Not Selected	0	Room_Type 1	57	2018	12	1	Online	0	0	0	79.20	2	Not_Canceled
26360	INN26361	2	0	0	2	Meal Plan 1	0	Room_Type 1	245	2018	6	17	Offline	0	0	0	75.00	0	Canceled
15193	INN15194	2	0	0	3	Meal Plan 2	0	Room_Type 1	36	2017	10	13	Offline	0	0	0	112.00	0	Not_Canceled
19873	INN19874	2	0	0	1	Not Selected	0	Room_Type 1	38	2018	7	2	Online	0	0	0	107.10	1	Not_Canceled
8015	INN08016	1	1	0	1	Meal Plan 2	0	Room_Type 1	29	2018	12	17	Online	0	0	0	130.00	0	Not_Canceled
36151	INN36152	2	0	2	2	Meal Plan 1	0	Room_Type 1	26	2018	2	7	Online	0	0	0	64.64	0	Not_Canceled
21254	INN21255	1	0	1	2	Meal Plan 1	0	Room_Type 1	6	2018	5	23	Online	0	0	0	97.02	1	Not_Canceled
16881	INN16882	2	0	1	0	Meal Plan 1	0	Room_Type 1	258	2018	10	16	Offline	0	0	0	110.00	0	Canceled
9465	INN09466	2	0	2	1	Meal Plan 1	0	Room_Type 4	118	2018	10	29	Online	0	0	0	104.40	1	Not_Canceled
18785	INN18786	1	0	0	2	Meal Plan 1	0	Room_Type 1	188	2018	6	15	Offline	0	0	0	130.00	0	Canceled
28097	INN28098	2	0	1	2	Meal Plan 1	0	Room_Type 1	39	2018	3	14	Offline	0	0	0	85.00	0	Not_Canceled
29239	INN29240	1	0	0	2	Meal Plan 1	0	Room_Type 1	32	2017	11	20	Offline	0	0	0	73.00	0	Not_Canceled
664	INN00665	2	1	1	1	Meal Plan 1	1	Room_Type 1	27	2018	8	8	Online	0	0	0	195.50	2	Not_Canceled
8285	INN08286	2	0	2	1	Not Selected	0	Room_Type 1	240	2018	12	10	Online	0	0	0	67.50	2	Canceled
4598	INN04599	2	0	1	0	Not Selected	0	Room_Type 1	127	2018	7	25	Online	0	0	0	94.50	1	Not_Canceled
21340	INN21341	2	2	0	3	Meal Plan 1	0	Room_Type 6	24	2018	4	14	Online	0	0	0	207.00	2	Not_Canceled
12098	INN12099	2	0	1	3	Meal Plan 1	0	Room_Type 1	262	2018	12	26	Online	0	0	0	73.95	0	Canceled
2904	INN02905	2	0	0	5	Meal Plan 1	0	Room_Type 4	41	2018	10	4	Online	0	0	0	139.50	2	Not_Canceled
21902	INN21903	2	0	2	3	Meal Plan 1	0	Room_Type 4	101	2018	5	5	Online	0	0	0	132.60	1	Not_Canceled

	type_of_meal_plan	room_type_reserved	market_segment_type	booking_status
count	36275	36275	36275	36275
unique	4	7	5	2
top	Meal Plan 1	Room_Type 1	Online	Not_Canceled
freq	27835	28130	23214	24390

	no_of_adults	no_of_week_nights	lead_time	arrival_year	arrival_month	arrival_date	avg_price_per_room	booking_status	length_stay	no_of_weekend_nights_log	no_of_special_requests_log	type_of_meal_plan_Not Selected	market_segment_type_Offline	market_segment_type_Online
0	2	2	224	2017	10	2	65.00	False	3	0.693147	0.000000	0	1	0
1	2	3	5	2018	11	6	106.68	False	5	1.098612	0.693147	1	0	1
2	1	1	1	2018	2	28	60.00	True	3	1.098612	0.000000	0	0	1
3	2	2	211	2018	5	20	100.00	True	2	0.000000	0.000000	0	0	1
4	2	1	48	2018	4	11	94.50	True	2	0.693147	0.000000	1	0	1

	Model	Train_Recall	Test_Recall
0	Initial decision tree model	0.981	0.792
1	Decision tree with restricted maximum depth	0.732	0.739
2	Decision treee with hyperparameter tuning	0.732	0.739
3	Decision tree with post-pruning	0.979	0.794